Project Summary/Abstract The etiology of many complex human diseases/disorders is multi-factorial involving the contribution of genetics, environmental exposures as well as complicated interactions between them. As living organisms, people are exposed to multiple environmental risk factors, such as chemical contaminants and non-chemical stressors (e.g., nutrients intake, hormone level and stress) on a daily basis. There is clear evidence that disease risk can be modified by simultaneous and sequential exposure to multiple environmental factors, larger than what would be expected from simple addition of the effects of the factors acting alone. Thus, the ?single environmental exposure? approach cannot capture the combined environmental effect and their synergistic interactions with our genetic system. Built upon our previous methodology development on GE interactions, the long-term goal of the research is to understand and gain novel insights into how environmental mixtures jointly moderate genetic influences on disease risk with longitudinal genetic data. Our objective is to develop powerful statistical methods to understand how genes interact with multiple environmental exposures as a whole to affect disease risk and to further dissect the dynamics of GE effects. Specifically, we try to address: 1) Which genetic variants are sensitive to multiple environmental exposures to affect disease risk? 2) Which mixtures of environmental exposures are responsible for the risk? and 3) What is the dynamics of the synergistic GE effects over time? Non- and semi-parametric methods will be developed to model and test synergistic GE effect with longitudinal data. We will apply the methods to a longitudinal study of GE interactions on eating disorder (ED) and explore the mechanism of gene by hormone interaction on woman?s eating behavior. We will provide efficient estimation and testing procedures with asymptotic evaluations. User friendly computational tools will be made available for public use. The success will provide important tools to facilitate the process of disease gene identification, and advance the discovery of novel genes and environmental risk factors to facilitate identification of drug targets and better prevention strategies to enhance public health. In addition, novel genetic and environmental interaction findings based on the ED GWAS data will likely provide new insights into the etiology of eating disorder in women.